OpenAI’s New Scorecard: Measure AI ROI Like a Capybara
Quick answer
OpenAI's new AI scorecard helps developers measure ROI with four practical metrics: useful work, cost per task, dependability, and return on compute.
OpenAI’s CFO, Sarah Friar, just dropped a practical AI scorecard to help developers measure ROI without drowning in buzzwords. Think of it as a swamp-friendly compass for navigating the murky waters of AI investment.
What’s in the Scorecard?
The scorecard focuses on four key metrics that matter to builders:
- Useful Work: How much actual, valuable output does your AI produce? Not just fluff.
- Cost per Successful Task: What’s the real price tag for each job well done?
- Dependability: Can you count on it when the swamp gets choppy?
- Return on Compute: Are your GPU cycles paying off?
This isn’t just for the big caimans with unlimited budgets. It’s for every capybara building in the AI swamp, from indie hackers to enterprise teams.
Why This Matters
We’ve all seen AI projects that look great on paper but sink when it’s time to deliver. This scorecard gives you a clear channel to measure success, so you can avoid those deep pools of regret. Pair it with tools like Vercel for deployment or Supabase for backend, and you’ve got a solid stack.
For more on AI pricing, check our model pricing comparison.
Original announcement published on OpenAI.